CDF Cold Framework

The CDF (Cumulative Distribution Function) framework, particularly in its "cold" variant (CDF-cold), focuses on improving the accuracy of forecasting, especially in scenarios with limited or missing historical data (the "cold-start" problem). Current research emphasizes integrating causal inference with deep learning models to better capture relationships between variables in multivariate time series data, employing algorithms that address the challenges posed by non-stationary data and incomplete cumulative distribution functions. This work has significant implications for various fields requiring accurate predictions from incomplete data, such as network traffic forecasting and online market analysis, by enhancing the reliability and robustness of forecasting models.